Abstract
This paper presents an algorithm for the segmentation and classification of dense range images of industrial parts. Range images, are unique in that they directly approximate the physical surfaces of a real world 3-D scene. The segmentation of images (range or intensity) is based on edge detection or region growing techniques. The approach presented in this paper segments range images by combining edge detection and region growing techniques. Jump and roof edges are detected using residual analysis. The residual is defined as the absolute value of the difference between the original image and a filtered version. We show that, at an edge, the difference after smoothing has a maxima in the direction perpendicular to the edge for jump and roof edges. The segmented surfaces is then classified into planar, convex, or concave. The classification is done in two steps. The first step utilizes a variation of the Wald-Wolfowitz runs test to classify the surfaces into planar or curved. The second step further classifies each curved surface into convex or concave using a multi-scale residual computation. The performance of the algorithm on a number of industrial parts range images is presented.
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References
G. J. Agin and T. O. Binford, “Computer description of curved objects,” in Proc. 3rd Int. Joint Conf. Artificial Intelligence, pp. 629–640, 1973.
E. Al-Hujazi and A. Sood, “ Range data description based on multiple characteristics,” in Proc. NASA Conf. on Space Applications of Artificial Intelligence, pp. 295–309, 1988.
E. Al-Hujazi and A. Sood, “Segmentation of range images,” in Proc. SPIE Conf. on Application of Artificial Intelligence, Orlando, Florida, pp. 229–238, 1989.
P. Besl and R. C. Jain, “Segmentation through variable order surface fitting,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-10, no. 2, pp. 167–192, Mar.1988.
M. Brady, J. Ponce, A. Yuille, and H. Asada, “Describing surfaces,” Comput. Vision, Graphics, Image Processing, vol. 32, pp. 1–28, 1985.
W. J. Conover, “Practical nonparametric statistics”, 2nd ed. New. York: Wiley, 1980.
R. Duda, D. Nitzan and P. Barrett, “Use of range and reflectance data to find planar surface regions,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-1, no. 4, pp. 259–271, July 1983.
T. G. Fan, G. Medioni and R. Nevatia, “Description of surfaces from range data using curvature properties,” in Proc. Computer Vision and Pattern Recognition Conf., IEEE Comput. Soc., pp. 86–91, 1986.
O. Faugeras and M. Hebert, “The representation, recognition, and locating of 3-D objects,” Int. J. of Robotic res., Vol.5, pp. 27–52, 1986.
P. J. Flynn and A. K. Jain, “Surface classification: Hypothesis testing and parameter estimation,” in Proc. Computer Vision and Pattern Recognition Conf., IEEE Comput. Soc., pp. 261–267, 1988.
J. H. Friedman and L. C. Rafsky, “Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample test,” Ann. Math. Stat., no. 7, pp. 697–717, 1979.
W. E. L. Grimson and T. Pavlidis, “Discontinuity detection for visual surface reconstruction,” Compu. Vision, Graphics, Image Processing, vol. 30, pp. 316–330, 1985.
M. Hebert and J. Ponce, “A new method for segmenting 3-D scenes into primitives,” in Proc. 6th Int. Conf. Pattern Recognition, pp. 836–838, 1982.
T. C. Henderson, “Efficient 3-D object representation for industrial vision systems,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-5, no. 6, pp. 609–617, Nov. 1983.
R. Hoffman and A. K. Jain, “Segmentation and classification of range images,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-9, no. 5, pp. 608–620 Sep. 1987.
S. Inokuchi, T. Nita, F. Matsuday and Y. Sakurai, “A three-dimensional edge-region operator for range pictures,” in Proc. 6th Int. Conf. Pattern Recognition, pp. 918–920, 1982.
D. Ittner and A. Jain, “3-D surface Discrimination from local curvature measures,” in Proc. Computer Vision and Pattern Recognition Conf, IEEE Comput. Soc., pp. 119–123, 1985.
D. Lee and T. Pavlidis, “Edge detection through residual analysis,” in Proc. Computer Vision and Pattern Recognition Conf, IEEE Comput. Soc., pp. 215–222, 1988.
Y. Muller and R. Mohr, “Planes and quadrics detection using the Hough transform,” in Proc. 7th Int. Conf. Pattern Recognition, pp. 1101–1103, 1982.
M. Oshima and Y. Shiria, “Object recognition using three-dimensional information,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-5, no. 4, pp. 353–361, July 1983.
S. P. Smith and A. K. Jain, “Testing the uniformity in multidimensional data,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, no. 1, pp. 73–81, Jan. 1983.
A. Wald and J. Wolfowitz, “On a test whether two samples are from the same population,” Ann. Math. Statist., 11, 147–162.
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© 1992 Springer-Verlag Berlin Heidelberg
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Al-Hujazi, E.H., Sood, A. (1992). Residual Analysis for Range Image Segmentation and Classification. In: Sood, A.K., Wechsler, H. (eds) Active Perception and Robot Vision. NATO ASI Series, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77225-2_26
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DOI: https://doi.org/10.1007/978-3-642-77225-2_26
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